Smart Meter Consumption

Alexander Martin Tureczek, Per Sieverts Nielsen

Research output: Other contributionNet publication - Internet publicationCommunication

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Abstract

For more than a decade researchers have successfully analyzed smart-meter data to identify consumption patterns. Numerous projects have applied K-Means and other clustering algorithms from machine learning to identify various consumption patterns hidden in the smart-meter data. What motivates both researchers and private stakeholders is the possibility of producing consumption-clustering solutions applicable outside academia to facilitate value propositions for both utilities and consumers. However, for clusters to be truly applicable beyond academia, they need to be defined in such a way that they are meaningful and stable. Therefore, it is important to study the stability of the clusters across time periods to ensure that cluster solutions remain the
same and that the transition between clusters is understood and quantified.
Original languageEnglish
Publication date2020
PublisherTechnical University of Denmark
Number of pages2
Publication statusPublished - 2020

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